Mixed effect models of longitudinal Alzheimer's disease data: a cautionarynote

Longitudinal studies of cognitive function in Alzheimer's disease (AD) pati ents are powerful tools to better understand the biology and natural histor y of the disease, but the attributes of the studies that make them valuable also pose special challenges to analysts. A fundamental problem is the acc urate measure of time at which cognitive decline begins. Investigators typi cally use the date of AD diagnosis or the date of enrolment in an AD study. If the rate of cognitive decline is non-linear, variables associated with the time of diagnosis or enrolment might artificially be associated with th e rate of decline. Unlike the mixed effects models typically used to analys e cognitive decline, summary measure analyses do not directly compare the r ate of decline with time since decline began, and, therefore, are less sens itive to biased measures of time of decline. We simulated trajectories of c ognitive decline using the multivariate normal random effect model and test ed the ability of the two analytic techniques to discriminate between true and spurious associations. Our analyses suggest summary measure models are less likely to detect spurious associations generated by biased measures of time at which decline begins, and more likely to detect true associations concealed by biased time measurement. Copyright (C) 2000 John Wiley & Sons, Ltd.